EPAM Systems
Global technology engineering leader with 62,000+ engineers delivering enterprise-scale AI/ML across 50+ countries.
Founded 1993 | Newtown, PA, USA | 62,000+ employees | Last updated: July 2026
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What is EPAM Systems?
EPAM Systems is a global technology engineering company founded in 1993 and headquartered in Newtown, Pennsylvania. The company employs 62,000+ engineers across 50+ countries and is publicly traded on the NYSE. EPAM provides end-to-end AI development services from strategy and consulting to implementation and support, working with Fortune 500 clients across financial services, healthcare, retail, media, and government. EPAM is the largest firm in this review, with AI/ML capabilities delivered within a full-service technology engineering operation.
EPAM Systems was founded in 1993 and is headquartered in Newtown, PA, USA. The firm employs 62,000+ people and works primarily with clients in financial services, healthcare, retail, media, government sectors. Its primary differentiator is: 62,000+ engineers across 50+ countries delivering ML inside a full-service technology engineering operation — unmatched scale and compliance depth for global enterprise AI programmes.
EPAM Systems tech stack and services
PythonTensorFlowPyTorchAzure MLAWS SageMakerApache SparkDatabricksKubernetesJavaMLflow
| Service area | Details |
|---|---|
| Global enterprise AI transformation programme requiring multi-country deployment and governance | Available for financial services, healthcare, retail, media, government clients |
| Complex Fortune 500 ML programme integrating across dozens of legacy systems | Available for financial services, healthcare, retail, media, government clients |
| Government-sector AI programme requiring maximum organizational stability and compliance controls | Available for financial services, healthcare, retail, media, government clients |
| Financial services ML for risk, fraud, and regulatory reporting at global scale | Available for financial services, healthcare, retail, media, government clients |
| Enterprise MLOps platform implementation for organizations managing hundreds of production models | Available for financial services, healthcare, retail, media, government clients |
EPAM Systems use cases
Short answer: EPAM Systems is best suited for large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration.
| Use case | Industries | Approach |
|---|---|---|
| Global enterprise AI transformation programme requiring multi-country deployment and governance | financial services, healthcare | Python, TensorFlow |
| Complex Fortune 500 ML programme integrating across dozens of legacy systems | financial services, healthcare | Python, TensorFlow |
| Government-sector AI programme requiring maximum organizational stability and compliance controls | financial services, healthcare | Python, TensorFlow |
| Financial services ML for risk, fraud, and regulatory reporting at global scale | financial services, healthcare | Python, TensorFlow |
| Enterprise MLOps platform implementation for organizations managing hundreds of production models | financial services, healthcare | Python, TensorFlow |
EPAM Systems pricing
Short answer: EPAM Systems uses a dedicated team, t&m, fixed project, staff augmentation pricing approach. Minimum engagement starts at $50K.
| Engagement model | Typical range | Best for |
|---|---|---|
| Dedicated team | Variable; depends on team size | Large programmes or team augmentation |
| T&M | Variable; depends on team size | Large programmes or team augmentation |
| Fixed project | From $50K | Well-defined scope |
| Staff augmentation | Variable; depends on team size | Large programmes or team augmentation |
EPAM Systems does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
EPAM Systems pros and cons
| Advantages | Things to consider |
|---|---|
| +62,000+ engineers provides unmatched scale for simultaneous large-scale enterprise ML programmes | -Enterprise scale means ML projects go through larger organizational process — slower initiation than boutiques |
| +Publicly traded NYSE company with audited financials — maximum organizational stability and governance | -High minimum engagement ($50K) limits accessibility for SMBs or early-stage organizations |
| +Global delivery across 50+ countries enables ML delivery under local data sovereignty requirements | -Generalist technology engineering scope means ML specialist depth may be lower per individual than pure-play ML boutiques |
| +Full AI lifecycle from strategy through production MLOps within one organizational relationship | |
| +Fortune 500 client base validates enterprise-grade ML delivery at the highest complexity level |
EPAM Systems vs alternatives
How EPAM Systems compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| Tensorway | Teams needing a dedicated ML specialist boutique with... | ML-only focus with a dedicated specialist team backed by 25 years of Anadea software delivery infrastructure — unusually deep for a firm of this size | 4.8 | Full comparison |
| LeewayHertz | Enterprises seeking end-to-end AI/ML product delivery with a... | Product-centric AI delivery culture with verified Fortune 500 client references including ESPN, Siemens, and 3M — now operating within The Hackett Group | 4.6 | Full comparison |
| InData Labs | Mid-market organizations with specific, complex ML problems requiring... | Pure-play ML boutique with a measurably higher specialist-to-generalist ratio than typical service firms, confirmed by Clutch as a top AI service provider | 4.5 | Full comparison |
| HatchWorks AI | Companies seeking AI-native teams that embed generative AI... | Clutch #1 AI Services Company with a proprietary Generative Driven Development methodology claimed to reduce delivery time by 30–50% (per company website; independently unverifiable) | 4.4 | Full comparison |
| STX Next | Organizations that need ML models operationalized inside complete... | Europe's largest Python-specialist firm uniquely positioned to embed ML into production software without the integration friction that plagues pure-play ML boutiques | 4.3 | Full comparison |
| Tredence | Enterprise teams that need last-mile ML adoption —... | Industry-specific AI accelerators and a proven focus on last-mile ML adoption, closing the execution gap between data science output and real business value | 4.3 | Full comparison |
| Addepto | Mid-market companies in finance, energy, or retail needing... | End-to-end AI/ML delivery with particular sector depth in financial services and energy — industries that require compliance sophistication alongside technical capability | 4.2 | Full comparison |
| DataForest | Data-first companies needing robust data engineering infrastructure as... | Data engineering-first approach builds pipeline and data quality foundations before model development, addressing the root cause of most ML project failures | 4.2 | Full comparison |
| Forte Group | Organizations needing the engineering discipline of a larger... | Structured AI service lines with Tier 1 delivery rigor and specialist consultancy agility — serving organizations that need both without enterprise-tier pricing | 4.1 | Full comparison |
| Binariks | Healthcare, fintech, and insurance organizations needing ML built... | Compliance-first ML engineering for regulated industries — governance and audit trails are built in from the architecture stage, not retrofitted after launch | 4.1 | Full comparison |
| Softeq | Hardware manufacturers and industrial companies needing ML integrated... | Unique capability to combine hardware design expertise with ML engineering, deploying models at the edge where cloud-only ML firms cannot operate | 4.1 | Full comparison |
| Markovate | Retail, travel, and fitness platforms needing ML-powered recommendation... | 300+ delivered projects spanning recommendation systems, computer vision, and dynamic pricing, with deeper consumer-facing ML specialization than most comparably sized firms | 4.0 | Full comparison |
| ScienceSoft | Established enterprises needing ML consulting from a vendor... | 35+ years of enterprise delivery experience with a mature ML practice — providing compliance readiness, institutional knowledge, and process maturity rare in younger ML-focused competitors | 4.0 | Full comparison |
| Miquido | Product teams needing ML embedded inside polished digital... | Google-certified AI/ML capability paired with strong product design — clients receive ML that works inside well-crafted user experiences, not bolted-on algorithms | 4.0 | Full comparison |
| Simform | Industrial and enterprise companies needing cloud-native ML on... | AWS Premier Partner with 1,000+ engineers and documented depth in industrial IoT ML — connecting physical sensor streams to cloud ML inference at production scale | 3.9 | Full comparison |
| Intuz | Small and mid-size businesses needing custom AI/ML solutions... | 1,700+ project track record with a discovery-first engagement model making enterprise-grade ML accessible to SMBs through risk-reduced fixed-price POC phases | 3.9 | Full comparison |
| Scopic | Organizations needing fully custom ML engineering with 20+... | 20+ years as a distributed software company gives Scopic strong custom ML engineering discipline with confirmed production deployments across transportation and healthcare | 3.9 | Full comparison |
| N-iX | Enterprises needing large-scale ML engineering capacity in Eastern... | 2,400+ engineers with deep specialization in scalable AI architectures, able to field large dedicated teams for complex multi-year ML programmes at competitive Eastern European rates | 3.9 | Full comparison |
| Oxagile | Media, AdTech, and sports companies needing ML with... | 20+ years of video domain expertise uniquely positions Oxagile for ML use cases involving video understanding, visual search, and real-time video analytics | 3.8 | Full comparison |
| Innowise | Regulated industry organizations — banking, agriculture, healthcare —... | Cross-vertical ML delivery with documented case studies in banking automation, agricultural forecasting, and healthcare diagnostics — unusual breadth across regulated industries | 3.9 | Full comparison |
| Intellectsoft | Enterprises in fintech, healthcare, and construction needing ML... | Palo Alto HQ with 10 global delivery offices combining US-based account management with competitive Eastern European delivery rates for enterprise ML programmes | 3.8 | Full comparison |
| DataRoot Labs | Startups and scale-ups needing AI strategy alongside execution,... | One of Ukraine's most recognized ML consultancies — combining strategy-level AI advisory with hands-on engineering, a combination rare at this team size and price point | 3.8 | Full comparison |
| Itransition | Enterprises needing ML integrated into complex legacy software... | 25+ years of enterprise software delivery with five dedicated R&D labs, giving clients a mature delivery operation with advanced ML research support at competitive rates | 3.9 | Full comparison |
| 10Pearls | US-based enterprises and government contractors needing AI-native delivery... | AI-native engineering culture with four CRN Solution Provider 500 recognitions and 1,400+ experts spanning North America and LATAM for enterprise AI programmes | 3.8 | Full comparison |
| Coherent Solutions | Midwest enterprises and Microsoft-stack organizations needing ML capabilities... | Ranked #1 IT consulting firm in the Twin Cities five times in six years with 2,000+ engineers across 10 development centers, offering enterprise ML at competitive rates | 3.8 | Full comparison |
| Iflexion | US-based organizations needing ML integrated into complete custom... | 25 years of enterprise software delivery with 850+ professionals embedding ML into complete systems rather than delivering standalone models that require separate integration work | 3.7 | Full comparison |
| Appinventiv | Global businesses needing mobile-first ML delivery at scale... | 1,600+ specialists with a mobile-first AI approach and global footprint delivering 1,000+ digital assets with embedded ML — strong for consumer-facing AI product work | 3.8 | Full comparison |
| Avenga | Large enterprises in telco, banking, or automotive needing... | 6,000+ specialists across 44 delivery centers formed through PE-backed acquisitions, providing enterprise-scale AI delivery capacity — though cultural integration across legacy entities is ongoing | 3.7 | Full comparison |
| BairesDev | Companies needing rapid ML team scale-up using LATAM... | 4,000+ ML-capable LATAM engineers in US time zones with 1,200+ completed projects, enabling rapid scale-up for organizations that need to grow their ML capacity fast | 3.7 | Full comparison |
| Turing | Teams that need to extend their ML engineering... | AI-powered vetting platform screening 3M+ global ML developers to place the top 1% directly in client engineering teams at rates competitive with US in-house hiring | 3.7 | Full comparison |
EPAM Systems FAQ
What is EPAM Systems?
EPAM Systems is a global technology engineering company founded in 1993 and headquartered in Newtown, Pennsylvania. The company employs 62,000+ engineers across 50+ countries and is publicly traded on the NYSE. EPAM provides end-to-end AI development services from strategy and consulting to implementation and support, working with Fortune 500 clients across financial services, healthcare, retail, media, and government. EPAM is the largest firm in this review, with AI/ML capabilities delivered within a full-service technology engineering operation.
How much does EPAM Systems charge?
EPAM Systems uses dedicated team, t&m, fixed project, staff augmentation pricing. Minimum engagement starts at $50K. A discovery call is required to get project-specific quotes.
What tech stack does EPAM Systems use?
EPAM Systems works with Python, TensorFlow, PyTorch, Azure ML, AWS SageMaker, Apache Spark, Databricks, Kubernetes, Java, MLflow. Primary industries served include financial services, healthcare, retail, media, government.
Is EPAM Systems right for enterprise?
Large enterprises requiring ML at Fortune 500 scale with global delivery capacity, stringent compliance requirements, and complex multi-system integration. 62,000+ team size. Key consideration: Enterprise scale means ML projects go through larger organizational process — slower initiation than boutiques.
What are the best EPAM Systems alternatives?
The best alternatives to EPAM Systems depend on your use case. Top options are:
- Tensorway: ml-only focus with a dedicated specialist team backed by 25 years of anadea software delivery infrastructure — unusually deep for a firm of this size
- LeewayHertz: product-centric ai delivery culture with verified fortune 500 client references including espn, siemens, and 3m — now operating within the hackett group
- InData Labs: pure-play ml boutique with a measurably higher specialist-to-generalist ratio than typical service firms, confirmed by clutch as a top ai service provider
Compare EPAM Systems with other Machine Learning Development companies
EPAM Systems vs Tensorway EPAM Systems vs LeewayHertz EPAM Systems vs InData Labs EPAM Systems vs HatchWorks AI EPAM Systems vs STX Next EPAM Systems vs Tredence EPAM Systems vs Addepto EPAM Systems vs DataForest EPAM Systems vs Forte Group EPAM Systems vs Binariks EPAM Systems vs Softeq EPAM Systems vs Markovate EPAM Systems vs ScienceSoft EPAM Systems vs Miquido EPAM Systems vs Simform EPAM Systems vs Intuz EPAM Systems vs Scopic EPAM Systems vs N-iX EPAM Systems vs Oxagile EPAM Systems vs Innowise EPAM Systems vs Intellectsoft EPAM Systems vs DataRoot Labs EPAM Systems vs Itransition EPAM Systems vs 10Pearls EPAM Systems vs Coherent Solutions EPAM Systems vs Iflexion EPAM Systems vs Appinventiv EPAM Systems vs Avenga EPAM Systems vs BairesDev EPAM Systems vs Turing
Last reviewed: July 2026. Verify all details directly with EPAM Systems before making a decision.